oneDNN

This is a test of the Intel oneDNN as an Intel-optimized library for Deep Neural Networks and making use of its built-in benchdnn functionality. The result is the total perf time reported. Intel oneDNN was formerly known as DNNL (Deep Neural Network Library) and MKL-DNN before being rebranded as part of the oneAPI initiative.

To run this test with the Phoronix Test Suite, the basic command is: phoronix-test-suite benchmark onednn.

Project Site

github.com

Test Created

17 June 2020

Last Updated

20 December 2020

Test Maintainer

Michael Larabel

Test Type

Processor

Average Install Time

9 Minutes, 3 Seconds

Average Run Time

2 Minutes, 2 Seconds

Accolades

5k+ Downloads

Supported Platforms


Public Result UploadsReported Installs*Test Completions*OpenBenchmarking.orgEventsoneDNN Popularity Statisticspts/onednn2020.062020.072020.082020.092020.102020.112020.122021.014K8K12K16K20K
* Data based on those opting to upload their test results to OpenBenchmarking.org and users enabling the opt-in anonymous statistics reporting while running benchmarks from an Internet-connected platform.
Data current as of Mon, 18 Jan 2021 15:01:31 GMT.
Deconvolution Batch shapes_3d11.4%Recurrent Neural Network Training15.9%IP Shapes 1D11.5%Convolution Batch Shapes Auto11.5%Matrix Multiply Batch Shapes Transformer11.3%Recurrent Neural Network Inference15.7%IP Shapes 3D11.5%Deconvolution Batch shapes_1d11.4%Harness Option PopularityOpenBenchmarking.org
bf16bf16bf1614.1%u8s8f3240.0%f3245.9%Data Type Option PopularityOpenBenchmarking.org

Revision History

pts/onednn-1.6.1   [View Source]   Sun, 20 Dec 2020 09:58:16 GMT
This test profile builds and works fine on macOS so enable it (MacOSX).

pts/onednn-1.6.0   [View Source]   Wed, 09 Dec 2020 13:47:31 GMT
Update against oneDNN 2.0 upstream.

pts/onednn-1.5.0   [View Source]   Wed, 17 Jun 2020 16:26:39 GMT
Initial commit of oneDNN test profile based on Intel oneDNN 1.5, forked from existing mkl-dnn test profile that is named from MKL-DNN before it was renamed to DNNL and then oneDNN. So create new test profile to match Intel naming convention.

Suites Using This Test

Multi-Core

Machine Learning

Intel oneAPI

CPU Massive

Server CPU Tests

Creator Workloads

HPC - High Performance Computing


Performance Metrics

Analyze Test Configuration:

oneDNN 1.5

Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU

OpenBenchmarking.org metrics for this test profile configuration based on 551 public results since 17 June 2020 with the latest data as of 1 December 2020.

Below is an overview of the generalized performance for components where there is sufficient statistically significant data based upon user-uploaded results. It is important to keep in mind particularly in the Linux/open-source space there can be vastly different OS configurations, with this overview intended to offer just general guidance as to the performance expectations.

Component
Percentile Rank
# Matching Public Results
ms (Average)
100th
17
0.7 +/- 0.1
76th
10
3.0 +/- 0.2
Mid-Tier
75th
> 3.0
73rd
11
3.3 +/- 0.4
73rd
10
3.3 +/- 0.4
58th
7
5.9 +/- 0.7
Median
50th
6.6
50th
11
6.9 +/- 0.7
43rd
19
9.8 +/- 0.1
36th
13
15.5 +/- 1.7
33rd
7
16.4 +/- 0.2
33rd
9
16.5 +/- 3.8
32nd
5
16.7 +/- 0.3
31st
7
17.1 +/- 0.1
28th
5
18.0 +/- 0.4
27th
6
18.9 +/- 0.7
27th
3
18.9 +/- 0.4
Low-Tier
25th
> 19.4
25th
6
19.8 +/- 0.7
22nd
4
20.7 +/- 0.4
22nd
3
20.9 +/- 0.1
21st
3
20.9 +/- 0.1
18th
3
21.5 +/- 0.2
14th
3
23.0 +/- 0.2
14th
5
23.0 +/- 3.3
12th
18
23.2 +/- 3.9
11th
3
24.0 +/- 0.2
9th
3
26.9 +/- 0.6
8th
4
28.4 +/- 3.1
7th
6
29.4 +/- 2.4
7th
3
30.2 +/- 0.8
5th
3
34.6 +/- 0.7
3rd
3
48.6 +/- 2.1
1st
3
72.0 +/- 17.0
OpenBenchmarking.orgDistribution Of Public Results - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU551 Results Range From 0 To 558 ms024487296120144168192216240264288312336360384408432456480504528552576110220330440550

Based on OpenBenchmarking.org data, the selected test / test configuration (oneDNN 1.5 - Harness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPU) has an average run-time of 2 minutes. By default this test profile is set to run at least 3 times but may increase if the standard deviation exceeds pre-defined defaults or other calculations deem additional runs necessary for greater statistical accuracy of the result.

OpenBenchmarking.orgMinutesTime Required To Complete BenchmarkHarness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPURun-Time246810Min: 1 / Avg: 1 / Max: 1

Based on public OpenBenchmarking.org results, the selected test / test configuration has an average standard deviation of 0.5%.

OpenBenchmarking.orgPercent, Fewer Is BetterAverage Deviation Between RunsHarness: Convolution Batch Shapes Auto - Data Type: f32 - Engine: CPUDeviation246810Min: 0 / Avg: 0.55 / Max: 4

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